计算机与数字工程2024，Vol.52Issue(3)：827-833,879,8.DOI:10.3969/j.issn.1672-9722.2024.03.033
基于渐进多尺度注意力残差网络的单幅图像去雨方法
Single Image Rain Removal Method Based on Progressive Multi-scale Attention Residual Network
摘要
Abstract
Rains can exert severely impacts on the visibility of scenes,reducing the quality of imaging and affecting a large number of computer vision tasks and systems,such as video surveillance and self-driving car and the like.Eliminating rain streaks,therefore,is a crucial task.This paper proposes a novel deraining model,coined as progressive multi-scale attention residual net-work(PMARnet),to remove rain streaks from a single frame image.Considering that complex scenes usually consist of multiple rain layers,PMARnet contains several stages.Each of them possess a residual network to alleviate gradient vanishing.In further,a multi-scale fusion attention residual model(MAR)is proposed to better characterize the semantic feature and local spatial feature in detail for each rain layer.Two publicly available benchmark datasets,Rain100H and Rain100L,are used for experimental valida-tion.Compared with eleven existing advanced methods,PMARnet performs the best with an PSNR of 28.06 and an SSIM of 0.89 for Rain100H and accordingly 37.25 and 0.98 for Rain100L.Compared with the second best method,there is an improvement of 2.41%and 1.14%for Rain100H and that of 3.16%and 1.03%for Rain100L.In this study,the proposed PMARnet can effectively propa-gate information between the rain streaks layer and the clean background image layer.PMARnet makes good use of rain streaks layer and background layer,and can achieve good rain removal effect.关键词
单幅图像去雨/深度学习/渐进式图像去雨/多尺度融合/注意力网络Key words
single image rain removal/deep learning/progressive image deraining/multi-scale fusion/attention network分类
信息技术与安全科学引用本文复制引用
顾小豪,王欢..基于渐进多尺度注意力残差网络的单幅图像去雨方法[J].计算机与数字工程,2024,52(3):827-833,879,8.基金项目
国家自然科学基金项目(编号:61703209)资助. （编号:61703209）
